Discrete or continuous optimization can be used as a method for canonical polyadic (CP) decomposition of the MM tensor. Either the decomposition is sought in the discrete domain, where the factor matrices may have elements only in the set \(\lbrace 0,1,-1\rbrace \) , or the elements can be arbitrary, real or even complex-valued. One possibility to do the discrete optimization is to revert the problem to the problem of satisfiability of boolean equations [1]}, see, e.g., [2]}, [3]}. In continuous optimization, the most remarkable results were obtained by Smirnov [4]}. More advanced decomposition methods have been proposed [5]}, [6]}, and they are still being tested. Both discrete and continuous optimization might benefit from seeking symmetric decompositions [7]}, [8]}.
